Abnormal Data Detection and Identification Method of Distribution Internet of Things Monitoring Terminal Based on Spatiotemporal Correlation

Nan Shao,Yu Chen

doi:10.3390/en15062151

Abstract

As an important part of the ubiquitous power Internet of Things, the distribution Internet of Things can further improve the automation and informatization level of the distribution network. The reliability of the measurement data of the low-voltage terminal unit, as the sensing unit of the sensing layer of the distribution Internet of Things, has a great impact on the fault processing and advanced applications of the distribution Internet of Things. The self-check and the equipment working status monitoring of the main station of the low-voltage terminal unit struggle to identify the abnormality of measurement data. Aiming at this problem, an abnormal data detection and identification recognition method of a distribution Internet of Things monitoring terminal is proposed on the basis of spatiotemporal correlation. First, using the temporal correlation of monitoring terminal data, the proposed composite temporal series similarity measurement criterion is used to calculate the distance matrix between data, and the abnormal data detection is realized via combination with the improved DBSCAN algorithm. Then, using the spatial correlation of the data of the terminal unit, the geometric features of the spatial cross-correlation coefficient of the terminal nodes are extracted as the input of the cascaded fuzzy logic system to identify the abnormal source. Lastly, the effectiveness of the method is verified by a practical example.

Highlights

As an important part of the ubiquitous power Internet of Things, the distributionInternet of Things can effectively improve the automation and informatization level of the distribution network [1,2] and provide users with diversified and differentiated energy services; further enhancing the level of electricity safety for customers [3,4]
The perception layer is located at the end of the distribution Internet of Things, and it can transmit the information collected by a large number of low-voltage terminal units (LTUs) in the distribution network to the application layer through the network layer, providing data support for the functions of fault detection and low-voltage load monitoring in the distribution Internet of Things [5]
Aiming at the problem of abnormal data detection, this paper proposes an abnormal detection and identification method of LTU nodes in the distribution Internet of Things

Summary

Introduction

As an important part of the ubiquitous power Internet of Things, the distribution. Internet of Things can effectively improve the automation and informatization level of the distribution network [1,2] and provide users with diversified and differentiated energy services; further enhancing the level of electricity safety for customers [3,4]. The architecture of the distribution Internet of Things is divided into a perception layer, a network layer, a platform layer, and an application layer. The perception layer is located at the end of the distribution Internet of Things, and it can transmit the information collected by a large number of low-voltage terminal units (LTUs) in the distribution network to the application layer through the network layer, providing data support for the functions of fault detection and low-voltage load monitoring in the distribution Internet of Things [5]. Data anomalies can be divided into two categories

Methods

Results

Conclusion